Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI)
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1 Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI) PCORnet Best Practices Sharing Session Wednesday, August 5, 2015
2 Introductions to the Round Table Joseph Lucas, PhD Associate Director, Health System Operations, Information Initiative at Duke Adjunct Associate Research Professor of Statistical Science, Duke University Ben Neely, MS Biostatistician, Duke Clinical Research Institute Rachel Richesson, PhD, MS, MPH, FACMI Associate Professor, Duke University School of Nursing Shelley Rusincovitch Project Leader, Applied Informatics & Architecture
3 The round table on August 5, from left to right: Host Kristin Newby, MD, MHS; Shelley Rusincovitch; Rachel Richesson, PhD, MS, MPH, FACMI; Benjamin Neely, MS; Joseph Lucas, PhD
4 Phenotyping Objectives and Statistical Design Joe Lucas, PhD
5 Population Health Who are the at risk population? Can we identify them from EMR? Intervention before secondary morbidity/mortality Appropriately identified patients lead to more accurate treatments Improvement in accuracy of retrospective studies Financial incentives: Accountable care Can intervene early to lower future cost? Better assessment of future risk (higher reimbursement from payers)
6 Identifying patients: Diabetes What are the performance characteristics of an algorithm for identifying patients? Sample and compare to truth Disease status is not always clear in the EMR What is the gold standard truth?
7 Stability Suppose: We have 100,000 patients in the all zeros strata We sample 40 patients from this strata 1000 patients with disease in other strata MLE estimate of number of patients with disease If 0/40 have disease: 1000 If 1/40 have disease: 3500 This has drastic consequences for sensitivity estimates Bayesian approach, prior distributions Don t get real estimates of sensitivity until we sample at least one false negative Sensitivity: tp (tp+fn)
8 Uniform Sampling for Uncommon Disease Test and disease positive in 3% of the population Odds ratio 50 Odds disease given positive test over odds disease given negative (tp/fp) / (fn/tn) We can improve estimates of PPV by over-sampling patients with positive tests Sensitivity depends on estimating false negatives
9 Stratified Sampling Suppose we instead sample preferentially from patients with positive test tp PPV = tp+fp can be estimated well NPV and Specificity are dominated by a very low false negative rate We can trade sample size to get a better estimate of PPV
10 Definition 2 Multiple Computable Phenotypes Multiple definitions Stratify based on definitions At least one stratum contains patients not identified by any definition Sensitivity: tp (tp+fn) True positive can be well estimated False negative is poorly estimated, but only in one of the strata All computable definitions have the same false negative rate in that stratum Example: Two definitions Definition % 1.8% 1 2.2% 1.6% Hard to get accurate estimate of false negative because events are so rare in the 0,0 strata However, inaccuracy is shared by all definitions Hard to be accurate in this box
11 Comparing definitions Estimates of sensitivity are indistinguishabl e Estimates of improvement in sensitivity clearly favor definition 2 Our stratification makes comparing definitions possible because they share false negative rates in the largest stratum.
12 Methods Overview Rachel Richesson, PhD, MS, MPH, FACMI
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15 The CPM-SEDI Phenotype Development Process
16 Methods Blinded review by 2 reviewers with adjudication (S. Spratt, MD) Reviewers diabetes experts (physicians and NPs) from DUHS Reviews conducted May December 2014 The Research Electronic Data Capture (REDCap) platform used for random assignment of charts to reviewers and the collection of data for each review. Reviewers trained on chart review in MAESTRO Care and REDCap (one-hour training session + Manual of Operations) The reviewers examined electronic charts for a defined time range ( ) to match the time period of the phenotype queries.
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19 Discussion of Results Ben Neely, MS
20 (Unpublished results in manuscript preparation)
21 Live demo: Visualization of False Positives Ben Neely, MS
22 Next Horizons and Improving Workflows Joe Lucas, PhD Ben Neely, MS
23 STEARNS SequenTial EstimAtion with Redcap and Shiny.
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25 Lessons Learned Shelley Rusincovitch
26 Themes 1. Precision of language is important 2. Gold standard clinical definitions can be challenging and nuanced 3. Reviewer concordance can be challenging and nuanced 4. Codes change
27 Precision of language is important Revascularization Coronary revascularization Coronary artery revascularization Myocardial revascularization Cerebral revascularization Revascularization of lower limb Revascularization of whole leg Revascularization of foot Revascularization of toe Slide acknowledgement and thanks to Michelle Smerek
28 In Summary In order for the phenotypers to find good definitions (FIT), it is essential that they know what we are looking for (PURPOSE) This process is iterative! The clinicians and statisticians give us initial requirements, we survey the landscape, circle back with any questions and to get clarification, and then resume the search. More regular communication among the parties will result in phenotype definitions that better fit our purpose. Slide acknowledgement and thanks to Michelle Smerek
29 Applicability, Broader Context, and PCORnet Considerations Rachel Richesson, PhD, MS, MPH, FACMI
30 Benefits of Sharing Phenotypes Development and conduct of new multi-site studies (interventional and observational) Efficiencies of re-using definitions and code Comparability of EHR-derived data sets Comparison of study results and aggregation of evidence Reporting of data sets or results (e.g., ClinicalTrials.gov, NIH) Description of research populations in medical journals
31 Desirable Features URU + U Understandable Reproducible Usable essential for pragmatic trials... Useful o Validation (results and methods) o Use data elements and coding systems that are widely implemented in EHR systems o Community acceptance -- Standardized across sites or research communities essential for multi-site studies...
32 32 PCORnet: the National Patient-Centered Clinical Research Network PCORnet s goal is to improve the nation s capacity to conduct CER efficiently, by creating a large, highly representative, national patient-centered clinical research network for conducting clinical outcomes research. The vision is to support a learning US healthcare system, which would allow for large-scale research to be conducted with enhanced accuracy and efficiency.
33 Guiding principle: Make research easier Analysis ready data Reusable analysis tools Administrative simplicity Simple, pragmatic studies integrated into routine care A national/regional resource to answer questions important to patients, clinicians, and delivery system leaders A foundation of the Learning Health System
34 PCORnet Approach to Phenotypes Networks share phenotypes with CC Strongly encourage harmonization across PCORnet Encourage public posting (PheKB) by researchers
35 The greatest challenge part 1 Sufficient & appropriate documentation: standard phenotype definitions: identify, store, promote, implement
36 The greatest challenge part 2 Communication Clinical, scientific, statistical, data science, & technical experts Multiple users, stakeholders Research sponsors Disease and patient advocates Collaboration
37 Acknowledgements We gratefully acknowledge the leadership of Susan Spratt, MD, and thank the dedicated team of chart reviewers. We acknowledge and appreciate the individual contributions from members of the Center for Predictive Medicine and our collaborators in this work.
38 Acknowledgements, continued The projects and the work described are supported in part by grant number 1C1CMS from the Department of Health and Human Services, Centers for Medicare & Medicaid Services, and in part by the Bristol Myers Squibb Foundation Together on Diabetes program. These contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies.
39 Contact Information Joseph Lucas, PhD Associate Director, Health System Operations, Information Initiative at Duke Adjunct Associate Research Professor of Statistical Science, Duke University Ben Neely, MS Biostatistician Duke Clinical Research Institute Rachel Richesson, PhD, MS, MPH, FACMI Associate Professor, Duke University School of Nursing Shelley Rusincovitch Project Leader in Applied Informatics and Architecture Duke Translational Research Institute (DTRI)
40 Discussion
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